A Limb Compliant Sensing Strategy for Robot Collision Reaction
Bibliographic record
Abstract
This paper introduces a compliant limb sensor (CLS) concept for collision detection during robot-human contact. The CLS consists of an external rigid shell compliantly connected to the robot link with collision inferred from measured shell displacements. Measuring displacement of a rigid shell allows customizable compliance and high sampling rates due to the small number of required sensors. The proposed sensor is prototyped for the planar case using LED/light-to-voltage (LTV) sensors for shell pose measurement and foam as the compliant link between the shell and base. A physically motivated model for the output of LED/LTV sensor pairs is formulated for the estimation of the shell pose. Voltage measurements of redundant LTVs and a calibrated shell model are used with an iterative optimization routine to estimate the shell pose at high frequencies. Sensor performance is tested using five trajectories: rest, compression, shear, rotation, and arbitrary motion. Experiments confirmed that the CLS can sense the presence, direction, and intensity of impact. The potential application of the proposed sensor to safety in physical human-robot interaction is discussed. The novel sensing methodology also enables a new method of 3-D human-computer interaction due to the ability to modify the compliance and operating range of the CLS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".